We simulate our experiments using Raspberry Pi 2 as our user device with Google Speech Commands (GKWS) [1]} and UrbanSound8K (US8K) [2]} datasets across 10 FL iterations/communication rounds using our proposed framework. In GKWS, we choose the keywords: Yes, No, Up, Down, Left, Right, On, Off, Stop and Go, and perform regular Mel-frequency Cepstral Coefficients (MFCC) extraction as performed in [3]}, with sampling frequency of 14400 HZ. The MFCC data is divided into 20 windows and each window is of size 50 ms. US8K, an environmental sound dataset, consists of 10 classes of sound events: air conditioner, car horn, children playing, dog bark, drilling, engine idling, gun shot, jackhammer, siren and street music. We perform similar preprocessing as performed in GKWS for US8K as well.
